Summary of Gamedx: Generative Ai-based Medical Entity Data Extractor Using Large Language Models, by Mohammed-khalil Ghali et al.
GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models
by Mohammed-Khalil Ghali, Abdelrahman Farrag, Hajar Sakai, Hicham El Baz, Yu Jin, Sarah Lam
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A generative AI-powered approach called GAMedX is introduced for extracting entities from medical narratives and unstructured text. This method utilizes Large Language Models (LLMs) for Named Entity Recognition (NER), addressing a critical gap in current information extraction techniques. By leveraging the capabilities of LLMs, GAMedX efficiently extracts entities from unstructured text generated throughout various phases of patient hospital visits. The methodology integrates open-source LLMs with chained prompts and Pydantic schemas for structured output to navigate medical jargon. The paper presents a significant ROUGE F1 score on one evaluation dataset, achieving an accuracy of 98%. This innovation enhances entity extraction, offering a scalable and cost-effective solution for automated forms filling from unstructured data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GAMedX is a new way to get important information out of medical records. It uses special language models to find key words like patient names and medical conditions in unstructured text. This helps make it easier to extract useful data, which can be used for things like automating forms filling. The results show that GAMedX is very accurate, with a score of 98%. This innovation has the potential to improve healthcare by making it easier to process large amounts of information. |
Keywords
» Artificial intelligence » F1 score » Named entity recognition » Ner » Rouge
